ZiadSheriif/IntelliQuery
A semantic search indexing system designed to efficiently retrieve top matching results from a database of 20 million documents. Given the embedding of a search query, it quickly identifies and returns the most relevant documents
This project helps data engineers or machine learning practitioners build extremely fast semantic search capabilities for massive document databases. It takes embedded search queries and a database of up to 20 million embedded documents, and rapidly returns the most relevant documents. This is for anyone needing to implement efficient, scalable semantic search.
No commits in the last 6 months.
Use this if you need to quickly retrieve semantically relevant documents from a very large database using pre-computed embeddings.
Not ideal if you're looking for a user-facing search application or a tool that generates document embeddings for you.
Stars
11
Forks
4
Language
Jupyter Notebook
License
MIT
Category
Last pushed
Nov 20, 2024
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/embeddings/ZiadSheriif/IntelliQuery"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
AmenRa/retriv
A Python Search Engine for Humans 🥸
AKSW/sante
The Ontology, Dataset and Knowledge Search Engine
gnes-ai/gnes
GNES is Generic Neural Elastic Search, a cloud-native semantic search system based on deep...
raphaelsty/cherche
Neural Search
erenisci/wikipedia_synonym_search
Semantic search engine over Turkish Wikipedia. Uses a 3-stage pipeline (MongoDB → Word2Vec →...